Recidivism Research Is a Valuable Tool for Making Decisions Affecting Security, Classification, Movement, Programs and Release Planning

“Whys” and “Hows” of Measuring Jail Recidivism

Paper submitted for Jail Reentry Roundtable June 27-28, 2006 Urban Institute

Martha Lyman, EdD. Director of Research Hampden County Sheriff’s Department

Stefan LoBuglio Chief, Pre-Release and Reentry Services Division Montgomery County Department of Correction and Rehabilitation Introduction At community meetings, budgetary hearings, and even family and social occasions, jail administrators are often asked how frequently inmates in their facilities cycle from release to return, and what, if anything, can be done to slow this revolving door. What may appear as simple and logical questions actually prove difficult for most administrators to answer with great precision because few systems measure recidivism rates regularly and lack the capacity in terms of staff and data systems to report on these statistics. Unlike state prison systems, local correctional agencies typically do not have research departments, and other entities such as county-based criminal justice coordinating committees that might undertake such analyses, rarely do.

How is it then that leaders of public institutions that process millions of individuals through their doors and other criminal justice stakeholders should remain so seemingly uninterested in one obvious performance measure. A school superintendent who did not report on student drop-out rates or a hospital administrator who failed to review treatment morbidity data would surely be ousted by their governing boards, and perhaps charged with neglect if they failed to take action when these measures indicated a problem. But then a mission of a school is to educate and graduate students and that of a hospital to improve health care, whereas rarely is the mission of a jail defined as reducing recidivism.

Historically, jails have served as receiving institutions for individuals who have been remanded to custody through the interrelated actions and decisions of a variety of authorities including police, prosecutors, judges, immigration officials, parole and probation officers, and even psychiatric medical professionals. Understandably, jail administrators focus their energies on ensuring that they provide constitutional-abiding, clean, and humane facilities. Their days are consumed with issues of managing bed space, addressing overcrowding, scheduling round-the-clock staffing, tracking overtime and budget expenditures, and addressing the daunting challenges of attending to medical, dietary, and other care issues for individuals who move in and out of the institution with great velocity and who have great needs. As such, the value of

computing recidivism for a highly mobile population of individuals on completely different legal statuses – some pre-trial, some sentenced, some awaiting deportation, some awaiting further psychiatric evaluation – seems complicated and questionable, and largely explains the absence of interest or effort to tract this statistic.

This paper should provide both solace to jail administrators that these are indeed difficult questions to answer directly and forthrightly, but also a challenge that measuring recidivism rates for portions of jail populations is not only feasible but critical in helping a system manage scarce correctional resources and addressing overcrowding issues. Calculating recidivism rates gives administrators a better understanding of who is coming through the front door of their jails and how best to manage them. It can provide clues to changes in other parts of the criminal justice system that would otherwise go unnoticed.

Increases in community supervision violations might indicate a change in policy with probation and parole, an influx of individuals without residential address might reflect greater enforcement of nuisance ordinances by the polices, fewer bond releases might reflect overly cautious practices of new member to the judiciary, an increase in the number of inmates with mandatory sentences might indicate a decreased willingness in the prosecutor’s office for plea bargaining. While interesting in its own right and helpful for planning program interventions, these data can also serve as the basis for inter-agency dialogues about jail overcrowding and placing some of the burden and responsibility of prioritizing populations for finite jail bed space back on the institutions remanding them to custody.

To illustrate some of the considerations of computing recidivism and the manner in which a jail system can productively use this information, this paper uses the Hampden County Correctional Center in Ludlow Massachusetts as a case study. Since 1998, the Hampden County Sheriff’s Department has conducted an on-going study of recidivism and has generated a database of over 15,000 individuals. The Department has focused on sentenced offenders – approximately 40% of their inmate population –

and reports one and three-year rates. Importantly, recidivism rates are computed for many subpopulations by offense type, demographic backgrounds, custody status, and program participation including security status at the time of release. The annual report serves to validate the Department classification procedures by confirming that those released from its community-based programs have lower recidivism rates than those released from medium and maximum units. It has used this information to identify population trends that affect jail overcrowding and has shared this information with other players in the criminal justice system to launch collaborative initiatives. In this paper, the methods of the study are described by using the recidivism data collected for sentenced inmates who left the Department’s correctional facilities and programs in 2004. It will also describe future plans to build on this study to conduct rigorous program evaluations and to extend some of this methodology to the Department’s pre- trial population.

Understanding Jail Recidivism Jail and prison populations differ so significantly that measuring recidivism rates among these populations requires different considerations. The nation’s jail population flows like a torrential river with upwards of 12 million individuals moving in and out of institutions annually with an average daily population of 750,000. The total admissions and discharges for the nation’s jails in one month can be half of those entering and leaving all state and federal prisons annually. Most jail inmates will spend only hours or days in the institution before being released to the street or transferred to other institutions, however, an estimated 20% will spend at least one month, 12% at least two months, and 4% will spend more than 6 months.1

By contrast, offender flow into the nation’s prisons can be likened to a stream into a larger reservoir of an average daily population of 1.5 million with upwards of 700,000 individuals entering and leaving each year into state and federal facilities. They serve longer sentences than jail inmates: the average state prison sentence is five years and

1 Vanessa St. Gerard, “New Study Proves Jails are an Important Component of the Reentry Equation” One the Line, American Correctional Association, March 2005, Volume 28. No. 2.

the average federal sentence is even longer (between 5-10 years). While prison inmates have complicated legal issues that they continue to face while they are serving time, they largely come into the institution post-conviction and have more orderly and planned departures. For this population, recidivism studies are more straightforward and common. Some state correctional systems such as Pennsylvania do have research departments that publish recidivism statistics, and typically present recidivist data for different subpopulations by offense type and release category (i.e. parole, medium, minimum, etc.). Typically, they do not produce recidivism rates for individual institutions. In June 2002, the United States Department of Justice’s Bureau of Justice Statistics published one of the largest and best known studies that found that among nearly 300,000 prisoners released in 15 states in 1994, 67.5% were rearrested within 3 years, 46.9% were convicted for a new crime and 51.8% were re-incarcerated2.

Besides sheer numbers, jails also significantly differ from prisons in their operations. Jails serve a variety of functions in the criminal justice system from holding individuals pre-trial, holding individuals temporarily (juveniles, mentally ill, military, court witnesses, protective custody), holding individuals awaiting transfer to a state or federal agencies (often due to overcrowding), or incarcerating offenders serving post-conviction sentences. In many jurisdictions, offenders sentenced to one year or less serve time in jails although the sentence threshold between serving time locally and serving time in the state system ranges from none – all sentenced offenders are sent to the state prison system – to upwards of 30 months as is the case for the state of Massachusetts.

In conducting a recidivism analysis, researchers must carefully define that portion of the jail population that is released to the community and is “at risk” to recidivate. It would be meaningless to calculate the recidivism rates for jail inmates transferred to another correctional authority or who are deported, and their inclusion in a study could understate the true recidivism rate of the population. While in jail serving time for one offense, jail inmates often face additional legal issues related to

2 Patrick A. Langan & David J. Levin, “Recidivism of Prisoners Released in 1994,” Bureau of Justice Statistics (June 2002, NCJ 193427).

immigration, pending charges on other cases, or have outstanding detainers and warrants. Recidivism rates can be overstated if the dispositions of these pre-existing involvements with the criminal justice system are treated as new recidivist events post- release.

There are a number of significant inconsistencies among recidivism studies that seriously limit their usefulness for comparative analysis between agencies, states or programs. The most problematic of these is the lack of a consistent operational definition of recidivism. The four most common measures for recidivism are re-arrest, re-arraignment, reconviction and re-incarceration. Most studies of state prisons define recidivism as re-incarceration back to their correctional system. Many program-based studies use re-arrest and reconviction. There is also the question of what constitutes re- offending. Most state and federal agencies include technical violations of release conditions (probation or parole), but not all. Most studies do not include recidivistic activity that occurs out-of-state or even outside their system, largely due to lack of available data. An inmate released from a state correctional system who is re- incarcerated in a nearby jail in the same state, would not count as a recidivist in many reported studies. In an ideal world, corrections and other criminal justice agencies would utilize a common operational definition of recidivism, but unless and until that occurs, all studies must clearly state the criteria they used to define recidivism.

Do high recidivism rates indicate that a correctional system is not working? Few correctional officials would agree, particularly those that manage jails. They take issue with the private sector analogy often used that a business with a two thirds failure rate would go out of business, and would cite the profound differences between private industry and corrections. Corrections can not select or maintain quality control over the incoming “raw materials” that are sent by the court or by parole, and often has very little time to work and develop these materials into better products before release. With considerable criminological research backing, they would argue that the best predictors of continued criminal involvement are beyond their span of control and include offender’s age, criminal history, drug usage, and the families and neighborhoods to

which these individuals will return. They might add that such factors as the health of the economy and the enforcement, prosecutorial, and sentencing strategies of police, prosecutors, probation and parole agents, and the courts can negate the beneficial effects of programs and release preparation efforts that they might offer offenders in their facilities.

Further, not all jails and prisons incarcerate inmates with similar “risk” profiles, making comparisons of recidivism rates across different inmate populations, different geographical areas, and different corrections systems problematic. Nearly half of the nations 3,600 jails have populations under 50, yet the almost 160 jails with average daily populations of more than 2000 inmates incarcerate 30% of the total number of inmates in the country. Some jails are run by tribes, others by states, others by Sheriff’s, others by appointed directors, and some are run by the federal government. A much higher recidivism rate in one jurisdiction compared to another may provide little information about their relative effectiveness. Just as higher morbidity rates for gerontologists compared to pediatricians would provide little useful information by which to judge the relative effectiveness of the practitioners, recidivism rates between two jails systems may have no intrinsic utility.

For a jail system, the chief value of recidivism analysis is less as an institutional performance measure than as a diagnostic tool to better understand how offenders are flowing throughout a region’s criminal justice system and to identify changes. Recidivism research is a valuable tool for making decisions affecting security, classification, movement, programs and release planning. It is also useful for tracking population trends, to project staffing needs, allocate resources and form community partnerships. Such analysis helps determine whether correctional resources are being used wisely and whether certain policy and programmatic changes are needed. For example, Hampden County’s recidivism analysis indicated that parole violators were increasingly becoming a larger fraction of the incoming jail population, and this led to a joint investigation and collaboration between the two agencies to change the parole decision making and the revocation processes.

Developing baseline recidivism rates for jails by offender characteristics and offense types can also prove helpful in the evaluation of specific education, treatment, and pre-release reentry programs3. Increasingly, outside funders and local policy makers will ask jail administrators to demonstrate that their requests for funding for a particular program includes a robust evaluation design that goes well beyond graduation statistics or testimonial letters. They want evaluations to address the rampant problem of self-selection (or creaming) by including a scientific design where the recidivism rates of program participants are compared with a comparable group of offenders that share similar characteristics (control group). While evaluations that randomly assign individuals between treatment and control groups remain the gold standard of evaluation design, they prove very difficult to implement within a correctional setting. However, a quasi-experimental design allows for the use a matched sample of offenders to serve as a control with a treatment group4, and institutions that have baseline recidivism statistics categorized in several dimensions can more easily carry out this method. It also addresses the pitfall of many jail program designs that compare the recidivism rates of program participants with national or state recidivism statistics without recognizing the significant differences that exist inherently between different correctional populations.

3 For an excellent distillation of the research literature on the estimated effectiveness of rehabilitative program, see the Washington State Institute for Public Policy January 2006 report, “Evidence-Based Adult Corrections Programs: What Works and What Does Not.” For a review of why many programs achieve few measured effects, see “Adult Correctional Treatment,” Gerald G. Gaes, Timothy J. Flanagan, Laurence L. Motiuk, Lynn Stewart Crime and Justice, Vol. 26, Prisons (1999) , pp. 361-426. They explain that that education and treatment programs often are not designed or optimized to reduce recidivism: “The design and delivery of educational programs has commonly violated many of the principles of effective correctional treatment . . . education programs in prison have not been directed to specific criminogenic needs of offenders, have not been part of a multimodal intervention strategy, have not considered responsivity effects, have not been tailored to address the needs of offenders in different risk classifications, and have not been adequately funded to permit the high doses of educational intervention that many offenders require.”

4 Matched comparison groups suffer from a problem that important unobserved characteristics, such as motivation, can not be “matched.” Recent methodological techniques such as the use of propensity scores attempt to address this selection problem (see papers by Donald B. Rubin).

Lessons from Tracking Recidivism: Hampden County’s Sheriff’s Department

1. Defining the At-Risk-of-Recidivating Population

For its study, the Hampden County Sheriff’s Department (HCSD) includes only those sentenced offenders who are at risk of recidivating at the time of release. It includes those who are released to the community at the expiration of their sentence, the payment of fines, or who receive parole. Sentenced offenders who are transferred to other correctional facilities at the time of release (immigration, another local jurisdiction, state or federal systems) are excluded. If included, these individual would bias downward the estimates of recidivism as they continue to remain confined and are not at risk of recidivating. HCSD also excludes pre-trial detainees from its studies. These individual have not been convicted of a crime and are still presumed innocent. Their eventual release path can prove complicated: some are bailed out, others have charges dismissed, some received sentences of time served, and so tracking their releases proves more challenging.

Within the institution, pre-trial detainees are also not required to participate in programming Although these exclusions mean that the study population constitutes only about 40% of the total releases each year, sentenced offenders utilize jail beds for much longer periods of time than those on pre-trial status. Also, they are required to participate in correctional programs, are eligible for movement to lower security and parole, and are provided with a detailed release plan when they leave. Tracking their post-release outcomes can provide valuable information on the effectiveness of correctional practices and programs.

2. Defining Recidivism Hampden County records recidivistic activity along three dimensions; re- arraignment, reconviction and re-incarceration for either a new offense or a technical violation of probation or parole within the state of Massachusetts. One and three-year recidivism rates are reported. More specifically, re-arraignment is defined as any court

appearances within the criminal court system in Massachusetts following release or a revocation hearing held by the Massachusetts Parole Board. Dispositions of new cases are recorded, and any guilty findings are recorded as a new conviction. Re- incarceration is defined as a sentence of any length to a state or county correctional facility in the state of Massachusetts for either a new offense or violation of probation or parole. Individuals who recidivate to correctional systems in other states are not tracked nor does the Department track individuals who have relocated or even died after release. As such, this omission can introduce some downward bias on the stated recidivism rate. However, as the BJS study found, the percentage of individuals who crossed state lines and offended in another jurisdiction was quite low in the fifteen states studied.

3. Tracking Recidivism

Each month, researchers at Hampden County run paper copies of the state’s criminal history records of monthly cohorts of released inmates from one year before and three years before. These records, which are managed by the Board of Probation, are often referred to as BOPS, and provide a chronological listing of all court activity tied to individual case dockets. These records are carefully scrutinized and the dates and offense types of new recidivist activity are recorded in a separate Excel database.

Within these categories, further definitions are necessary. For example, in Massachusetts, probation is an agency within the court system, and parole is a separate entity under the Executive Office of Public Safety, the state agency that also oversees corrections. Probation violation hearings occur in court and are recorded in the state’s criminal justice system tied chronologically to the court docket of the original offense. Parole revocation hearings are not recorded in the state criminal justice database as events although the individual’s return to custody can be deduced. Suffice to say that recording accurately these types of community supervision revocations are challenging and require interagency cooperation.

The paths that offenders travel in and out of jail are diverse and are littered with cases that remain unresolved, either awaiting disposition or requiring a period of post- release supervision. These open cases present a special challenge when they result in incarceration. A return to jail as the result of disposition of a previous case is not recidivism, as it is the result of an earlier act. Such an event must be recorded however, as it limits the individual’s risk to re-offend which in turn affects the recidivism rate.

4. Analyzing and Reporting Results

Hampden County uses statistical software to produce charts and tables for a detailed recidivism report each year. It begins with extensive descriptive statistics of the release cohort. These data are useful for monitoring trends in inmate population. Descriptive statistics such as offenses by zip code help to detect changes in crime rates among neighborhoods. Offense codes, sentence length and post-release supervision status allows for the monitoring of shifts in prosecution and sentencing practices. And assessment data provide information for program design that can help identify gaps in support services in the community.

Recidivism rates are calculated across many dimensions including socio- demographic characteristics, geographic areas, offense types, sentence length, classification-at -release, release type, criminal history, criminogenic risks and post- release supervision. Such detailed analyses of recidivistic activity by released offenders can help to identify those factors that correlate with the risk to re-offend. To illustrate these points further, several figures from the Department’s most recent report that depict one-year recidivism rates for sentenced inmates released in 2004 are presented at the end of this paper.

Figures 1 and 2 present the one-year recidivism rates by arraignment, conviction, and incarceration for those individuals released from 2004 (Figure 1) and from 2000 through 2004 (Figure 2). Not surprisingly, the recidivism rates by arraignment are the highest followed by conviction and incarceration. A simple arrest results in a recidivist event by arraignment, but a case may take months or years to reach full disposition of

either a conviction or acquittal. For those found guilty, sentencing dispositions may take additional time. Figure 2 shows that overall recidivism rates dropped slightly from 2000- 2004.

Figure 3 charts recidivism by offense type. In the case of violent crimes, the re- arraignment recidivist statistic is the largest (20.4%), re-conviction (14.8%) second, and re-incarceration (12.8%) last. However, many individuals who commit public order offenses (e.g. driving under the influence) are violated from community supervision directly back to prison, and the recidivism rates for this offense type are in the reverse order – 52.9% re-arraigned ; 45.7% re-convicted, and 39.4% re-incarcerated. Figure 4 computes one-year recidivism rates based on scores from a risk/needs assessment instrument administered to all incoming inmates. In essence, this table validates that this instrument, called the Level of Services Inventory Revised (LSI-R) screening test, is able to sort out offenders who are at higher risk to recidivate. Using arraignment, 60.2 of those who scored in the highest category risk category recidivated compared to 25.7% in the lowest scoring group.

Figures 5, 6, and 7 provide some insight into the role of post-release supervision on recidivism. The first figure’s pie chart shows that technical violations of probation and parole constituted nearly one-third of those who recidivated by incarceration. The bar chart on figure 6 shows that those inmates released from the Department’s minimum and community-based programs recidivated at much lower rates than those released from medium security levels. This could indicate either that the community- base programs better prepared inmates for release or it could be an artifact of the classification process. To explain further, it could be that the Department correctly sorted out those inmates at greater risk to recidivate and chose not step them down to community programs. Finally, Figure 7 shows that parole contributed significant to post- release re-incarceration – significantly more so than probation. No surprisingly, those with both forms of community supervision – probation and parole – had the highest rates of re-incarceration (30.6%).

Figures 8 graphically depicts the complexity of computing recidivism of jail inmates due to the complexity of the relationship with the court. After one year, 17% had new cases that resulted in arraigments that were awaiting disposition. These cases are recorded as recidivist events by arraignment. However, if these cases lead to convictions or incarcerations after the one-year period, these data would be recorded as recidivist-events in the three-year review. Also, 34% of the release cohort were still facing open cases that began prior to their release from jail that had not been adjudicated a year after release or remained open for a period of post-release probationary supervision. Finally, default warrants, which are issued from the courts when individuals fail to appear for a disposition, are recorded as a data element because they may lead to a return to custody, but are not recorded as a recidivist-event. Similarly civil restraining orders are civil proceedings that may lead to a criminal charge and are recorded in the database but not as recidivist events. After one year, 11.7% had default warrants and 15.1% had new restraining orders listed since their release.

Finally, the four pie charts of Figure 9 provides the Department with important information on “specialist” recidivists -- those who recidivate by committing offenses of the same type as their original governing charge that led to their previous incarceration. In each of the four categories of offense types, the data shows significant “specialist behavior.”

6. Policy Development

Hampden County continually uses the recidivism data to inform and support solid correctional practices. One recent research-based initiative has been a complete overhaul of correctional programs to specifically target criminogenic risks and needs as identified by the Level of Service Inventory screening administered to all incoming inmates. This has assured that offenders spend their time in custody addressing those issues that place them at risk to re-offend.

Hard data is also shared with other local criminal justice agencies through a number of community collaborations directed toward enhancing public safety. The Hampden County Reentry Collaborative brings together representatives from the Sheriff’s Department, local police, the Office of the District Attorney, and probation and parole agents to address high-risk offenders just prior to release. Release plans are reviewed and the consequences of re-offending are discussed with the offender. The availability of up-to-date information on these high-risk offenders may actually increase the ability to identify services that address their assessed needs although it can also increase the likelihood of detecting non-compliance behavior.

In 2001, a shift in policy by the Massachusetts Parole Board led to a nearly doubling in parole releases in Hampden County. What followed was a flood of returns for technical violations, the overwhelming majority related to relapse. In response, the Hampden County Sheriff’s Department and Massachusetts Parole Board expanded a collaborative effort that had existed since 1996, bringing staff from both agencies together monthly to discuss how to address the problem. Staff from all levels of both agencies continue to work together to find more suitable home plans, deal with the shortage of treatment services in the community and explore intermediate sanctions. The key element in this collaboration is the sharing of information, much of it generated by the recidivism study. A similar effort with probation to match release conditions to assessed risks and needs in order to increase the likelihood of successful reentry is in the planning stages.

7. Future Developments

The evaluation design used at the HCSD has continued to evolve as new questions arise and priorities shift. This produces both positive and negative effects. Shifting focus keeps the research up-to-date and topical; however it can also produce inconsistencies in data from one year to the next, making comparisons more difficult. This has been countered by endeavoring to keep the core variables unchanged. New

variables are added each year as needed, and only a few have been dropped from the study.

Improvements in data collection are needed, most notably the difficulty in gathering data from the state’s criminal record database maintained by the Criminal History Systems Board. This is the primary source of data on criminal history in Massachusetts yet these records of court activity contain numerous data entry errors, inconsistencies in terminology and incomplete data. There is hope that this situation will improve with the implementation of new statewide data systems, however until this promise becomes reality, researchers are forced to rely on a number of manual and semi-automated techniques to address these data quality problems. Another issue involves probation violations. Probation violators comprise a large portion of the sentenced population, but information as to reason(s) for violation is sketchy at best. Efforts are underway to improve communication between the probation departments at the District and Superior Courts and the Sheriff’s Department. Because of Massachusetts’ proximity to the other New England states and New York, it is a concern that the criminal history record reports court activity only within Massachusetts, causing the study to possibly underestimate the recidivism rate. Unfortunately, the Department does not have the resources required to conduct the Interstate Information Index (Triple-I) on all releases, but the research staff are exploring the possibility of running a Triple-I on a random sample of releases to estimate the amount of recidivistic activity by offenders in neighboring states.

The obvious value of the information generated by recidivism research on the Department’s sentenced offenders, who represent only 40% of total releases, highlights the need to expand research to other segments of the population. For example, the Department’s current inmate census is approaching 200% of capacity, and the pre-trial count is at a record high. Managing this population requires new data that is more time- sensitive. As of May 1, 2006 Hampden County began entering all new pre-trial intakes into a database for the purpose of obtaining real-time information relative to court, bail status, time-in-custody, charges, and release type.

Pre-trial detainees represent a difficult challenge to track as most move quickly through the facility, while others stay longer in custody awaiting trial than most sentenced offenders. They also tend as a group to be higher risk and less stable. To better manage this group, the Department began administering the LSI screening to pre-trial intakes in 2002, and has utilized the screening results to implement pre-trial programs. Program participation provides a head start for pre-trial detainees who eventually changeover to sentenced; hopefully those who are bailed or released at court will carry some positive program effect with them.

Another group worthy of further study are those offenders sentenced to the state Department of Correction (DOC). Most will eventually return to local communities, and some will transition back through the Department’s jail. Hampden County has partnered with the Massachusetts Department of Correction on a “step-through” program that transfers eligible state inmates to the Hampden County House of Correction for the last 6 to 12 months of their sentence for movement through lower security and local release planning. These DOC Reentry inmates are currently included in the recidivism study; the Department would like to include all DOC inmates released directly to the Hampden County cities and neighborhoods including those who are not stepped-down through the Sheriff’s Department. .

Conclusions

Recidivism data for any single year provides only a static view of a number of dynamic phenomena. Despite this fact, very few agencies conduct ongoing recidivism studies. A brief examination of state prison web-sites conducted by the Massachusetts’ Department of Correction’s Research Division in 2004 revealed that fewer than half of the states produce recidivism reports, and even fewer produce them on an annual basis. While one-time studies can be useful in certain circumstances, ongoing studies of recidivism eliminate the risk inherent in relying on one release cohort as “typical” for the inmates from an institution over a long period of time, and can better track the effect of

changing external factors on the recidivism rate such as activity by police, courts, prosecutors, parole, economic conditions, and changes in public policy at the local, state and federal level.

The goal of an ongoing study of recidivism is to produce more than the “recidivism rate”. The broader purpose of such research should be to inform and support good correctional practices. Comprehensive study of recidivism involves much more than simply reporting rates of re-offending. It is a topic with many nuances that requires an investment in at least a few staff with research experience and a commitment to staff training. The returns however are invaluable. In the present climate of high inmate populations and shrinking resources it is more critical than ever to gather and report valid data on factors that may have an effect on the perpetuation of criminal behavior and to present that information in such a way that it is useful to public safety professionals in making security, classification, programming and release decisions that will improve each offender’s potential for successful reintegration into society and ultimately enhance public safety.

Selected Statistical Charts for the HCSD 2004 Sentenced Released Cohort Recidivism Study (N=2,298) 5

One-Year Recidivism Rates for 2004 Release Cohort of Sentenced Inmates by Arraignment, Conviction, & Incarceration

0.5 47.8% 0.4 0.3 25.5% 0.2 21.1% 0.1 0 Arraignment Conviction Incarceration

Figure 1

One-Year Recidivism Rates for Sentenced Released Cohorts in 2000 - 2004 by Arraignment, Conviction, and Incarceration

60 56.6 53.9 49.6 49.8 47.8 50

38.9 40 34.7 30.9 30.5 30.9 30 26.4 24.8 24.9 25.5 21.1 20

0 2000 2001 2002 2003 2004

Arraigned Convicted Incarcerated

Figure 2

5 The complete report of the Hampden County Sheriff’s Department’s 2004 Recidivism Study is available by request and copies will be placed on the resource table at the Jail Reentry Roundtable.

Arraignment, Conviction & Incarceration by Offense Type for 2004 Sentenced Released Cohort (%) 60

50 52.9 45.7 40 39.4 30

22.7 20 20.4 20.6 19.6 14.8 10 19.2 16.7 12.8 15.1 0 Violent Property Drug Public-Order

Arraignment Conviction Incarceration

Figure 3

One-Year Recidivism Rate (%) for 2004 Sentenced Release Cohort by LSI Risk Level

80 60.2 60 50.6 40 35.4 25.7 26.7 29.3 22.8 20 10.9 8.3 0 Arraigned Convicted Incarcerated

Low Medium High

Figure 4

Incarceration Reason by New Offense and Technical Violation for 2004 Sentenced Released Cohort

32%

68%

New Crime Technical Violation

Figure 5

Incarceration Rates (%) by Classification at Release for 2004 Sentenced Released Cohort

40 45.0

20 18.6 10 13.8 13.1 7.8 7.9 0 Day Reporting Minimum Medium

New Crime Technical Violation

Figure 6

Incarceration Rate by Post-Release Supervision Status for 2004 Cohort

35 30 25 28.4 30.6 20 15 19.1 10 14.8 5 0 Prob. Only Parole Both None Only

Figure 7

Disposition Status at the End of One Year for 2004 Sentenced Release Cohort 34.0 35 30 25 17.1 20 15.1 15 11.7 10 5 0 Open Open Warrants Restraining Cases-New Cases- Order Existing

Figure 8

Comparing Offense Type of Recidivist Offense with the Prior Offense Type of the Governing Charge for 2004 Sentenced Release Cohort

Violent Offenders Property Offenders

28.1 21.7 31.0 34.5

19.2 31.0 15.2 19.3

Violent Property Drug Public-Order Violent Property Drug Public-Order

Drug Offenders Public-Order Offenders

8.4 20.8 14.7 42.3 47.9 19.1 28.9 17.8

Violent Property Drug Public-Order Violent Property Drug Public-Order

Figure 9